Segmentation and classification of tobacco seedling diseases

In this paper, we present a novel algorithm for extracting lesion area and application of neural network to classify seedling diseases such as anthracnose and frog-eye spots on tobacco leaves. The lesion areas with anthracnose and frog-eye spots on a leaf of tobacco seedlings are segmented by contrast stretching transformation with an adjustable parameter and morphological operations. First order statistical texture features are extracted from lesion area to detect and diagnose the disease type. These texture features are then used for classification purpose. A Probabilistic Neural Network (PNN) is employed to classify anthracnose and frog-eye spots present on tobacco seedling leaves. In order to corroborate the efficacy of the proposed model we have conducted an experimentation on a dataset of 800 extracted areas of tobacco seedling leaves which are captured in an uncontrolled lighting conditions. The methodology presented herein effectively detected and classified the tobacco seedlings lesions upto an accuracy of 88.5933%. Further the recommended features are compared with Gray Level Co-occurrence Matrix (GLCM) based features to bring out their superiorities.